Systems and methods for managing an online user experience

ABSTRACT

Systems and methods are described for monitoring and managing an online experience. An example method includes: obtaining user data describing interactions between a plurality of users and plurality of online services; determining, based on the user data, a rate of change in usage of the online services for each user in the plurality of users; assigning a portion of the users to a user segment based on the user data and the determined rate of change in usage; and based on the assignment, providing each user in the user segment with a customized experience in one or more of the online services.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. App. No. 17/865,647, filed Jul. 15, 2022, which is a continuation of U.S. Pat. App. No. 17/016,219, filed Sep. 9, 2020, and issued as U.S. Pat. No. 11,392,666, on Jul. 19, 2022, which claims the benefit of U.S. Provisional Pat. Application No. 62/900,351, filed Sep. 13, 2019, the entire contents of each of which are incorporated by reference herein.

BACKGROUND

The following disclosure relates to systems and methods for monitoring and managing on online experience and, in certain examples, to systems and methods that enable online services to provide customized user experiences based on user interactions with the online services.

A large number and wide variety of online services are available to consumers who use client devices, such as media players, desktop computers, mobile phones, and smart TVs to access the services over the Internet. A streaming service provider, for example, can offer online streaming of movies, television programs, and/or music through a free or subscription-based service. Alternatively, users can download entire content files to their client devices prior to viewing or experiencing content. In addition to movies, television, and music, consumers can access online services related to gaming, e-commerce, communications, financial services, education, news, online storage, billing, customer relationship management (CRM), and/or marketplace online services. There is a need for an online service provider to provide an improved online experience by ensuring that users are taking advantage of all available benefits in the online service, to ensure higher user retention and more active user engagement with the online service.

SUMMARY

Systems and methods are described for monitoring and managing an online experience for users of one or more online services. An example method includes: obtaining user data describing interactions between a plurality of users and at least one online service; determining, based on the user data, a rate of change in usage of individual features and content within the online service for each user in the plurality of users; assigning a portion of the users to a user segment based on the user data and the determined rate of change in usage; and based on the assignment, providing each user in the user segment with a designated customized experience in one or more aspects of the online service. In various examples, a plurality of segments may be assigned one or more experiences and a user’s assignment within a segment may change over time based on variations in that user’s usage of the online service.

In one aspect, the subject matter of this disclosure relates to a computer-implemented method of monitoring and managing an online experience. The method includes: obtaining user data describing interactions between a plurality of users and a plurality of online services; determining, based on the user data, a rate of change in usage of the online services for each user in the plurality of users; assigning a portion of the users to a user segment based on the user data and the determined rate of change in usage; and based on the assignment, providing each user in the user segment with a customized experience in one or more of the online services.

In certain examples, obtaining the user data can include: providing a designation of one or more online items to be tracked, the online items including webpages, user interface buttons, and/or online events; receiving tracking data including a record of clicks, number of visits, and usage time corresponding to the online items; and aggregating the tracking data by user to obtain user data describing each user’s interactions with one or more of the online services. Each online service can include a website and/or a software application. The online services can provide content including video content, audio content, and/or text content. The rate of change can be indicative of declining usage of a first online service from the plurality of online services, and the customized experience can include an incentive or guidance to increase usage of the first online service.

In some implementations, the users can be assigned to one or more additional segments based on a churn score, a fraud score, a customer satisfaction score, a net promoter score, and/or a lifetime value. The customized experience can be delivered to all users of the user segment, and other users from the plurality of users may not receive the customized experience. The customized experience can be or include a hidden action in which a sequence of content is provided to the users of the user segment. The customized experience can be or include a path in which content is provided to users according to a predefined timeline. Providing the customized experience can include limiting a frequency of content presentations across the online services for each user in the user segment.

In another aspect, the subject matter of this disclosure relates to a system. The system includes one or more computer systems programmed to perform operations including: obtaining user data describing interactions between a plurality of users and a plurality of online services; determining, based on the user data, a rate of change in usage of the online services for each user in the plurality of users; assigning a portion of the users to a user segment based on the user data and the determined rate of change in usage; and based on the assignment, providing each user in the user segment with a customized experience in one or more of the online services.

In some instances, obtaining the user data can include: providing a designation of one or more online items to be tracked, the online items including webpages, user interface buttons, and/or online events; receiving tracking data including a record of clicks, number of visits, and usage time corresponding to the online items; and aggregating the tracking data by user to obtain user data describing each user’s interactions with one or more of the online services. Each online service can include a website and/or a software application. The online services can provide content including video content, audio content, and/or text content. The rate of change can be indicative of declining usage of a first online service from the plurality of online services, and the customized experience can include an incentive to increase usage of the first online service.

In certain examples, the users can be assigned to one or more additional segments based on a churn score, a fraud score, a customer satisfaction score, a net promoter score, and/or a lifetime value. The customized experience can be delivered to all users of the user segment, and other users from the plurality of users may not receive the customized experience. The customized experience can be or include a hidden action in which a sequence of content is provided to the users of the user segment. The customized experience can be or include a path in which content is provided to users according to a predefined timeline. Providing the customized experience can include limiting a frequency of content presentations across the online services for each user in the user segment.

In another aspect, the subject matter of this disclosure relates to a non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the one or more computer processors to perform operations including: obtaining user data describing interactions between a plurality of users and a plurality of online services; determining, based on the user data, a rate of change in usage of the online services for each user in the plurality of users; assigning a portion of the users to a user segment based on the user data and the determined rate of change in usage; and based on the assignment, providing each user in the user segment with a customized experience in one or more of the online services.

These and other objects, along with advantages and features of embodiments of the present invention herein disclosed, will become more apparent through reference to the following description, the figures, and the claims. Furthermore, it is to be understood that the features of the various embodiments described herein are not mutually exclusive and can exist in various combinations and permutations.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments of the present invention are described with reference to the following drawings, in which:

FIG. 1 is a schematic diagram of a system for monitoring and managing an online experience, according to an exemplary embodiment.

FIG. 2 is a screenshot of an example graphical user interface that allows an operator of an online service to specify combinations of traits or user segments to be tracked in the online service, according to an exemplary embodiment.

FIG. 3 is a screenshot of an example item of content presented to a user of an online service, according to an exemplary embodiment.

FIG. 4 is a screenshot of an example graphical user interface that can be used by an operator of an online service to configure one or more actions to be taken in response to user interactions with content presented in the online service, according to an exemplary embodiment.

FIG. 5 is a screenshot of an example graphical user interface that allows an operator of an online service to visualize an influence that items of content have on user retention in the online service over time, according to an exemplary embodiment.

FIG. 6 is a flowchart of an example method of monitoring and managing an online experience, according to an exemplary embodiment.

FIG. 7 is a block diagram of an example computer system, according to an exemplary embodiment.

DETAILED DESCRIPTION

It is contemplated that apparatus, systems, methods, and processes of the claimed invention encompass variations and adaptations developed using information from the embodiments described herein. Adaptation and/or modification of the apparatus, systems, methods, and processes described herein may be performed by those of ordinary skill in the relevant art.

It should be understood that the order of steps or order for performing certain actions is immaterial so long as the invention remains operable. Moreover, two or more steps or actions may be conducted simultaneously.

In various examples, the systems and methods described herein provide a real-time cloud infrastructure platform that enables real-time targeting of users for monitoring and managing an online user experience. The systems and methods can provide highly dynamic software applications (e.g., mobile applications or apps), websites, and other online services by, for example, segmenting users into actionable groups and providing one or more of the user segments with customized online experiences (e.g., unique content presentations and/or system features). The systems and methods can provide real-time, dynamic, rules-based paths to manage online content presentations, such that a single online service can deliver multiple experiences to different user segments. This can enable, for example, a differentiated experience for high value users versus fraudulent users, or customized experiences based on, for example, time of day, content availability, a user’s lack of use of certain features of the online service, or other measurable business dimensions.

In various examples, a “user” or “consumer” can be or refer to a person who uses or accesses an online service (e.g., using a website or an “app,” such as a mobile application, a desktop application, or a TV application) that is monitored and managed using the systems and methods described herein.

In various examples, a “customer” or “operator” can be or refer to an individual, company, organization, or other entity that owns or operates an online service accessed by users. The operator can utilize the systems and methods described herein to provide an improved or customized user experience in the online service.

In various examples, a “trait” can be or refer to an individual user attribute and/or behavior exhibited by a user. A trait can be or include, for example, a device type, a lifetime value (LTV), a fraud score, a churn score, a net promoter score (NPS), and/or a customer satisfaction score. Traits can include system traits that are provided by the systems described herein, such as geographical location, or custom traits that are unique to an online service, such as, for example, the number of times the user has accessed a specific feature or area of the online service, or annual revenue per user (ARPU). Traits can be aggregated or grouped into categories, such as, for example, device types, device locations, user characteristics, and/or other categories. Custom traits or trait categories can be specified by an operator.

In various examples, a “segment” can be or refer to a group of users that share a common set of traits. A segment can be or include, for example, a group of users sharing one or more of the following traits: high LTV, high NPS scores, high fraud scores, users in a trial subscription, trialist users with heavy usage, users making limited use of premium features, all connected TV users in a specified geographical region (e.g., California), a rate of change in the usage of specific features over a specified time period (e.g., a 50% increase week over week), or other traits. A user may belong to more than one segment, and there are generally no limits on the number of segments that may be created or the specific traits that can be combined to form a segment.

In various examples, a “promotion” (alternatively referred to as an “incentive” or “guidance”) can be or refer to content (e.g., text, video, images, and/or audio) that is shown or presented to an end user on the user’s client device in an effort to incentivize or encourage a desired user behavior and/or to provide guidance to inform the user of one or more features available in an online service. Promotions can be limited to, for example, specific segments of users, a maximum number of users, a time of day, and/or a day of the week. Any and all aspects of a promotion’s appearance can be modified or customized by an operator of an online service. A promotion can be implemented in or include, for example, one or more actions taken for one or more segments during a specific time or according to a calendar schedule. A promotion can be limited by a financial budget or a maximum number of users. A promotion can be presented on user client devices via a full screen overlay, a popup window, or other portion of a webpage or app (e.g., an inline placement).

In various examples, an “action” can be or refer to a task that a promotion or an experience is configured to perform when triggered or accepted by a user. An action can be or include, for example, extending a user’s free trial of an online service, adding a discount to a user’s purchase, upgrading or downgrading a user’s plan, guiding the user on the usage of a feature, providing access to content or areas of a site that might be of interest to the user, reducing or increasing an amount of advertisements shown, and/or opening a support ticket with a customized priority setting. Actions may be performed in a server side or client side manner, depending on the type of operation required.

In various examples, a “path” or “experience” can be or refer to a combination of one or more actions with one or more segments. The experience can remain in place for a specific time duration (e.g., a day, week, or month) or can be implemented according to a timeline or calendar schedule. Experiences may be invisible to the end user and/or may require no action by the end user to be initiated. For example, experiences can be triggered automatically or by an operator of an online service. In some instances, for example, an operator may activate VIP support and/or reduced ad load for members or users having high LTV.

FIG. 1 illustrates an example system 100 for monitoring and managing an online user experience. A server system 112 provides functionality for monitoring and managing user consumption of online services provided by a plurality of online service providers, including a first online service provider 114 and a second online service provider 116, which can be provided by one or more operators, as described herein. Any number of online service providers can be included in the system 100. The server system 112 includes software components and databases that can be deployed at one or more data centers 118 in one or more geographic locations, for example. In certain instances, the server system 112 is, includes, or utilizes a content delivery network (CDN). The server system 112 software components can include a tracking module 120 for tracking or monitoring user consumption, a segment module 122 for assigning or aggregating users into one or more segments or groups, and an action module 124 for taking one or more actions to provide user segments with customized or differentiated experiences in the online services (e.g., provided by the online service providers 114 and 116), as described herein. The software components can include subcomponents that can execute on the same or on different individual processors or data processing apparatus. The server system 112 databases can include a user data 126 database for storing data associated with one or more users of the online services. The data can include information describing or relating to each individual user’s activities associated with the online services. The user data 126 database can reside in one or more physical storage systems. The software components and data will be further described below.

In various examples, the online service providers 114 and 116 can provide online services to a plurality of users. The online services can be provided using, for example, a software application (e.g., a client-based application, such as a mobile or connected TV based application) or a web-based software application. The online services can relate to a wide variety of services, functions, and information, including, for example, entertainment (e.g., games, music, videos, movies, etc.), e-health, e-learning, business (e.g., word processing, accounting, spreadsheets, etc.), news, weather, finance, sports, etc. The users can access the online services through a network 130 (e.g., including the Internet) through the use of client devices, such as a tablet computer 140, a smart phone 142, and a personal computer 144. Other client devices are possible. For example, in some instances, the user client devices can include media streaming devices, such as an APPLE TV or a ROKU streaming device. In alternative examples, the user data 126 database or any portions thereof can be stored on one or more client devices and/or in databases associated with the online service providers 114 and 116. Additionally or alternatively, software components for the system 100 (e.g., the tracking module 120, the segment module 122, and/or the action module 124) or any portions thereof can reside on or be used to perform operations on one or more client devices and/or the online service providers 114 and 116.

In various examples, the system 100 and associated methods can provide a data-driven, real-time retention and engagement platform. The systems and methods can allow operators or managers of websites and apps (e.g., online service providers 114 and 116) to create data-driven retention messaging to end users powered by a rich targeting system that can continuously monitor individual product usage. In some instances, for example, one or more items of content (e.g., promotions) can be triggered based not only on a user’s usage characteristics but also based on time on webpages, button clicks, navigation history, or other user characteristics or activities. The system 100 can allow online service providers to build highly dynamic apps and websites, for example, by (i) segmenting users into actionable groups and/or (ii) converting fixed business rules and systems into flexible paths. The approach can be integrated into a backend or server directly (e.g., through experiences) or directly into an app or website (e.g., promotion).

In some implementations, the system 100 can utilize server-side code connections, which can make systems of online service providers highly dynamic and flexible, without complicated logic or programming. This can be achieved by extracting information from business systems or databases and attaching the information to incoming HTTP headers, so that such information can be accessible by the operator’s APIs and server side systems. The extracted information can include, for example, user traits, data merged from external databases and APIs, and/or actions defined within a path, as described herein. The architecture of the system 100 can achieve an always-on intelligence that enables online service providers to be more successful, self-sufficient, and/or private and less dependent on congestion-prone single points of processing.

Advantageously, in various instances, by creating a capability for real-time, dynamic rules-based paths to availability, quality, support, and/or promotions, a single app or website can deliver multiple experiences (e.g., customized user experiences) to different user segments, thereby enabling a differentiated or customized experience for different user groups (e.g., high LTV users vs. fraudulent users) or based on time of day, content availability, or any measurable business dimension. The system 100 can achieve these capabilities through the use of usage tracking, traits, actions, triggers, segments, promotions, and/or experiences, as described herein.

Usage Tracking

In certain examples, the systems and methods described herein can improve user retention and engagement for online services by tracking and storing each individual user’s consumption of features and content associated with the online services. The systems and methods can measure changes in a user’s consumption of one or more features and content over time (e.g., relative to the user’s past usage), for example, to determine whether the user is continuing to be engaged with or derive value from the service. In general, a truer picture of the user’s consumption of the site emerges when multiple features, content, and/or benefits are combined into a user segment that can be tracked.

For example, a video streaming site may offer users an option to watch content in high quality (e.g., 4K video), download videos, and/or view content on multiple devices in exchange for subscribing to a premium subscription plan. The systems and methods can monitor or determine how many (i) high quality videos a user views, (ii) how many videos the user downloads, and (iii) how many different devices the user uses to access the service. The systems and methods can then calculate changes and rates of change in these quantities (e.g., compared to a previous time period, such as previous day, week, or month) to determine whether the user’s rate of content consumption is increasing, decreasing, or staying the same. For example, a rate of content consumption can be determined for each user (e.g., videos per day or downloads per day), and changes in the rate of content consumption can be monitored over time.

In various implementations, content consumption tracking information can be used by the systems and methods to customize a user experience, for example, by determining whether to offer the user an incentive or inform the user about a feature or item of content the user may have missed or may be interested in viewing. For example, an operator of an online service can designate (e.g., using a graphical user interface or admin console) one or more webpages, app screens, buttons, or events on the service that should be tracked. A server (e.g., the server system 112) can then transmit the list of items to be tracked to a client agent or SDK on one or more user client devices (e.g., client devices 140, 142, and 144). Clicks, visits, timestamps, and related information can be collected (e.g., using the SDK) for the tracked pages, buttons, and/or events. The systems and methods (e.g., using the tracking module 120) can analyze the incoming content consumption information and compare the information with prior periods (e.g., days, weeks, or months). Based on the obtained information, the operators of the online services can create one or more user segments, for example, defined by one or more specifications, criteria, and/or thresholds. For example, an online service provider may wish to create a user segment based on consumption of a specific type of content, such as HD content. This can allow the service provider to identify users according to HD content consumption patterns or behavior. It may be important, for example, for the service provider to achieve a weekly 10% increase in HD content consumption among users. Such information can be monitored through the creation of one or more segments.

In certain examples, segments can be defined as a collection of one or more traits (e.g., user attributes or behavior) that can be tracked for each online service. Common traits can include, for example, how often a user visits a website or app, and/or how long the user stays during a visit. Additionally or alternatively, traits can include information related to user devices, locations, and/or business metrics, such as LTV or a customer satisfaction score. In some examples, traits can be normalized, for example, on a 0-10 scale, with a lowest value in the dataset normalized to 0 and a highest value normalized to 10. The normalization can be performed or adjusted in real time as new low values and high values are recorded. Such normalization can enable online service operators to more easily compare usage levels among users, without having to understand site-wide averages, highs, or lows. For example, a heavy user can be defined as a user whose minutes per visit is growing by 30% or more each week.

In some examples, certain traits such as lifetime value (LTV) and/or satisfaction score, can be ingested from an online service provider’s data source. This can be accomplished, for example, by obtaining a mapping of a user ID to the trait value (e.g., in a CSV or TSV file). The file can be stored and encrypted, if desired, on a secured database (e.g., Amazon S3) and/or can be ingested on a periodic basis to capture updates. As an alternative to ingesting such a file, trait data can be transmitted (e.g., from an online service provider to the server system 112) via SFTP and API mechanisms and/or scheduled to sync upon a preset frequency.

In some examples, a custom trait can be configured by an operator of an online service provider by specifying certain information, such as: a name or label for the trait (e.g., “Lifetime Value” or “NPS”); a data type (e.g., whether the trait is represented by a number, a Boolean value, a string, or a date); and/or a display type (e.g., a slider, checkbox, dropdown, multi select, range select, or text input box). Traits can include system traits, such as, for example: time in app (e.g., visit duration time spent by the user in the app in MM:SS), number of visits (e.g., number of times per day, week, or month a user visits an online service), geographic location (e.g., city, town, state, country, and/or postal code), device type (e.g., phone, tablet, laptop, desktop, TV, or watch), device OS (e.g., iOS, ANDROID, WINDOWS, or CHROME), connection type (e.g., 3G, LTE, WiFi, and/or carrier name), app version, concurrency level (e.g., a number of unique concurrent sessions using identical login credentials), fraud score (e.g., a number between 0 and 100 ranking the user’s likelihood of being a fraudulent user), user ID, days to renewal, and/or referrer (e.g., source website or partner name associated with first time visitors). Custom traits can include, for example, customer LTV (e.g., total time and/or money spent by user since first visit), customer since (e.g., months or years since customer account was created), trial about to end (e.g., true if user is within three days of trial end date), is delinquent (e.g., true if a last billing attempt was unsuccessful), customer satisfaction score (e.g., NPS or like score usually on a 1-10 scale), customer acquisition cost (e.g., a cost to acquire the user, usually determined by a referrer), number of tickets (e.g., for customer support) opened by user, last accessed support channel, purchase urgency (e.g., an indication based on historic data of a user’s willingness to acquire an item immediately and for immediate consumption), asset expiration date (e.g., a date and time when an item expires, which can be useful for modifying system behavior related to time sensitive events such as a sporting match or perishable items), and/or support traits (e.g., a number of open tickets, last accessed support channel, customer satisfaction score, or NPS score).

For example, FIG. 2 includes a screenshot 200 of an example graphical user interface that an operator of an online service can use to specify combinations of traits or user segments to track. The graphical user interface allows the operator to create new combinations of traits for future tracking purposes. Desired tracking configurations can be saved for subsequent use. A custom tracker for an item can include, for example, a name (e.g., “Downloads”), a one sentence description of the tracker, a type (e.g., whether the item being tracked is a page, track, or event), and/or a value (e.g., a value for the item). While a newly added tracker can begin collecting information immediately, it may take time (e.g., up to 24 hours) before the tracker has acquired enough information to produce meaningful results or targeting options.

In various examples, the systems and methods described herein can provide certain default or initial tracking capabilities (e.g., out-of-the-box, without a need for additional configuration by online service providers) related to one or more of the following usage characteristics for online services: number of visits, minutes per visit, location (city, state, postal code), and/or device (type, platform, operating system). Usage tracking can be customized further to track specific user actions within a website, page, or app user interface, such as, for example, a button click or a visited URL.

After data collection has been active for an initial period of time (e.g., two or more weeks), the systems and methods can provide a fraud score and/or a churn score for each user. The fraud score can indicate, for example, a level of fraud associated with a user. In some instances, for example, the fraud score can indicate that a user is a bot and/or a user who has a malicious intent. The fraud score can be a number between zero and 10 representing a likelihood that a user is a fraudulent user. The fraud score can be computed in real time for every visitor to the online service and can be available as a trait upon user arrival to the service.

Fraud calculations can be reputation-based, rules-based, or machine learning based. The reputation-based approach can involve calculating fraud scores utilizing known deny list databases from a variety of sources. Typical information for this purpose can include device identifier, email address, IP address, and/or phone number. The rules-based approach can be used to increase or decrease the reputation-based bases score according to one or more rules applied to various user characteristics. For example, the use of anonymizers with certain foreign IP addresses may be indicative of fraud and may boost the fraud score by 10%. The machine learning based approach for calculating the fraud score can be done in a supervised manner, for example, in which feature engineered data sets are fed to an active classifier to produce the fraud score. Behaviors specific to usage of an online service can be implemented using this method. In some examples, machine learning models can be trained to recognize user activity patterns associated with fraud and, once trained, the models can receive activity data for a user as input and provide the fraud score as output.

Additionally or alternatively, the churn score can indicate, for example, a level of churn or inactivity associated with a user. For example, a user who has stopped (e.g., for several days, weeks, or months) visiting an online service or accessing one or more available features or content can have a high churn score for that service. The churn score can be a number (e.g., between zero and one) indicating a probability or likelihood that a user will churn (e.g., stop using or leave an online service). Machine learning models can be used to calculate the churn score based on historic data about users who have churned from the service. For example, historic churn data can be imported and a custom trait can be provided that indicates a user’s retention status. The custom trait can be or include, for example, a date that a user’s membership ended or will end, and/or a field denoting whether a user is active or churned. In various examples, machine learning models can be trained to recognize user activity patterns associated with churn and, once trained, the models can receive activity data for a user as input and provide the churn score as output.

Actions

In various examples, an action can be or include one or more steps that are performed (e.g., in an online service) based on a user’s interactions with content, such as a promotion. The action can be or include, for example, preparing and sending an email to a mailing list each time a user accepts a promotion. The email can contain actionable details for an agent to perform the required promotion such as, for example, applying a discount or extending a membership. An action can be an item (e.g., an item of content or a user setting or status) within an application frontend or backend (e.g., on a client device or server, respectively) that can be dynamically altered and/or provided to a user. Examples of such items can include implementing coupons and associated content, support channels (e.g., customer support services available for only certain users), or changes to trial duration (e.g., extending a user’s free trial). Such action items can be specified as a name-value pair and can be available to the frontend or backend (e.g., as an HTTP header). Actions can trigger modifications to backend business systems or applications based on criteria specified within segments. This can be used, for example, to disable promotion codes for users in a high fraud score segment.

One type of action that can be taken can be referred to as a “hidden action,” which can involve taking steps to modify a standard user experience for one or more (e.g., users in a segment). The experience for users in a free tier (e.g., a trial subscription) may be adjusted, for example, such that these users receive a customized experience (e.g., more advertising or promotion content), compared to other users in a paid tier, or have trial periods automatically extended due to limited use. In some implementations, an operator of an online service that is targeting specific sets of users (e.g., utilizing segmentation and usage consumption tracking) can provide a customized user experience using hidden actions implemented using the systems and methods described herein. Hidden actions can allow the operator to specify a set of real-time actions that can automatically trigger when a targeted set of users is utilizing the online service (e.g., website or app).

In various examples, hidden actions can utilize or include third party connector actions, custom connector actions, and/or user experience properties. With third party connector actions, the systems and methods can interface with a set of online services to perform user specific actions on the services’ behalf. Such actions can include, for example, adding a user to an email campaign or support ticket, or creating an individualized user coupon (e.g., including image or video content). With custom connector actions, an operator of an online service can configure the systems and methods to call an existing API on the backend (e.g., on a server) of the operator’s system using appropriate authentication and API parameters, preferably in such a manner that no engineering changes are required on the operator’s system. Additionally or alternatively, user experience properties can allow an operator of an online service to define properties that signal the service (e.g., app and/or website) to modify or customize the experience for a specific user. Such modifications can include, for example, adjusting a display banner or video ad load, providing improved content quality (e.g., UHD quality video streams) for high value users, and/or providing cosmetic updates for certain users (e.g., VIP users). The cosmetic updates can include, for example, one or more changes to a layout, color scheme, text, images, video, or audio in a graphical user interface for the users.

In some examples, to implement a hidden action, an operator of an online service can use a console (e.g., an admin console or other user interface) provided by the systems and methods described herein to specify one or more segments that will receive a set of actions associated with the hidden action. The operator can use the console to specify an order in which the actions are performed. When a user logs in or visits the online service, the systems and methods described herein can determine (e.g., in real-time) which segment(s) the user belongs to. If the user belongs to a segment having an associated hidden action, the systems and methods can execute the associated actions (e.g., external and/or API actions) in the order specified. In some instances, when the hidden action or promotion contains client-side actions, such actions can be returned by the server for execution on a client device (e.g., a client SDK embedded within the user’s app or browser). In some instances, when a user is determined to be in a free tier, the systems and methods can initiate an external API action (e.g., to an ad server) to configure a parameter that controls how many ads should be shown to the user.

In some instances, rather than or in addition to initiating a single action (e.g., a subscribe action) in response to a user’s interaction with content (e.g., acceptance of guidance or a promotion), it can be desirable to initiate a series of sequential actions, which can be referred to herein as “cascading actions” or “cascading 1-click actions.” The sequence of actions can include, for example, a subscribe action, followed by changing a user status (e.g., to VIP status), followed by sending an email to the user (e.g., notifying the user of the status change). The systems and methods described herein are configured to enable an operator of an online service to define and implement cascading actions, which can include any combination of actions that may be modified at any time.

FIG. 3 includes a screenshot 300 of a promotion or guidance presented to a user. The user has several options for interacting with such content, and each option can trigger a specific set of cascading actions taken in response to the user’s interaction. In some instances, for example, a unique or distinct cascading action can be implemented for each type of user interaction, which can include, for example: a primary accept interaction 302 (e.g., a most prominent call to action triggered when a user clicks a “sign me up” or “accept” button), a secondary accept interaction 304 (e.g., a second most desired call to action triggered when a user seeks additional information related to the promotion), a decline interaction 306 (e.g., a user clicks a “no thanks” or “decline” button), a dismiss interaction 308 (e.g., a user closes the promotion), and/or a timeout interaction 310 (e.g., triggered by a configurable countdown timer that closes the promotion when an allotted time expires).

In various implementations, any action or set of cascading actions trigged by a user’s interaction with content (e.g., a promotion) can be or include a third party connector action, a custom connector action, and/or a website or client action. With a third party connector action, the systems and methods can interface with a set of third party services to perform user specific actions on the online service operator’s behalf. Such actions can include, for example, adding a user to an email campaign or a support ticket, or creating an individualized user coupon. With a custom connector action, an operator of an online service can configure the systems and methods to call an existing API on the online service’s backend (e.g., a server computer) with appropriate authentication and API parameters in such a way that no engineering changes are required from the operator. With a website or client action (e.g., on a website or client application), an operator of an online system can configure the systems and methods described herein with a snippet of code that can be executed (e.g., to generate content or a promotion) on a user’s client device when the user interaction takes place. This can enable real-time execution of logic that can rely on functionality and user context residing on a client application, yet may not require a recompilation of any application code used by the operator (e.g., on the client device).

FIG. 4 includes a screenshot 400 of an example graphical user interface (e.g., an admin console) that allows an operator of an online service provider to configure one or more actions to take in response to user interactions with content presented in the online service. The actions can include, for example, hidden actions or cascading actions. A unique set of actions can be taken in response to each type of user interaction with the content.

To implement a cascading action, an operator of an online service provider can use the admin console to define one or more actions associated with a promotion. A specific sequence for the actions can be specified, for example, by dragging user-interface elements for the actions into a desired order. An end user who visits the online service and qualifies to receive the promotion (e.g., based on user segment) can trigger the cascade of specified actions on a server computer (e.g., server system 112) for the systems and methods. The server can then call the associated external or custom API to execute the actions (e.g., on the user client device).

In general, after a user accepts a promotion by clicking on a primary or secondary accept call to action, the systems and methods can continue tracking the user’s usage to determine whether a goal of the operator has been satisfied. The goal can be defined (e.g., by the operator using the admin console) as a usage consumption tracker and can be attached to or associated with the promotion. Once an operator’s defined goal has been met, the systems and methods can include the tracking data in a collection of metrics made available to the operator, so that the operator can track performance of promotions, incentives, or other guidance.

In various examples, the actions performed by the systems and methods described herein can include, for example, built-in actions, custom API actions, website or client actions, connector actions, and/or support actions. Built-in actions can be or include, for example, sending an email (e.g., to a specified address when a user interacts with content), sending an SMS or text message (e.g., to a specified mobile number when a user interacts with content), or redirecting a user (e.g., to a specified URL when a user interacts with content). Custom API actions can allow an operator to specify API-driven actions to the backend (e.g., on a server) of the operator’s system. Custom API actions can be or include, for example, registration or sign up, add to favorites or bookmark, purchase, or share. Website or client actions can allow an operator to specify client-side JS actions that can be used within an item of content (e.g., a promotion). Website or client actions can be or include, for example, registration or sign up, add to favorites or bookmark, purchase, or share. Connector actions can be performed within third party business systems or backends. In addition to or instead of third party systems, a connector can be established to the backend of an online service provider, which in turn can enable the online service provider to perform advanced actions within a promotion. For example, a promotion to add a movie to a watchlist or add an item to a cart can be performed automatically or directly in response to one click by the user. Support actions can be or include, for example, showing or hiding a support channel or link to customer service (e.g., by changing a visibility of support channels on an embedded widget), assigning priority to a support ticket (e.g., set the priority to urgent, high, medium, or low) adding information to a ticket (e.g., add the visitor’s profile to the ticket, such as user ID, LTV, customer acquisition cost, etc.), adding tags to ticket (e.g., add predefined tags to a user who created tickets through the widget, such as high LTV, VIP, high fraud, etc.), setting ticket state (e.g., setting a status on a ticket to new, open, pending, solved, or hold), and/or adding info to chat (e.g., adding a visitor’s profile to chat, such as user ID, LTV, customer acquisition cost, etc.). The systems and methods described herein can include or provide a support module that facilitates dynamic connections between a website and the website’s support backend. Depending on the support platform, additional traits like number of tickets for the user, last accessed support channel, and customer satisfaction/NPS score can be supported or considered.

Promotions

In various examples, a promotion or guidance can be or include content that is presented (e.g., in a full screen overlay or inline) to users within one or more segments. The content can hide the page content and/or can occupy most of the available width and height of a user’s display screen. All aspects of the promotion content can be configured, including, for example, text, appearance, buttons, actions, color scheme, and interactivity. Promotions can optionally be set to trigger during specific events like a button click, a visit to a specific URL, and/or time on a page. Additionally or alternatively, promotions can be scheduled to appear at a specific date and time and/or be limited to a certain number of users and/or a specific budget (e.g., specified by an operator of an online service). Custom goals can be set on promotions, and such goals can be used to provide visibility into how well the promotion is converting against internal success metrics. In some implementations, an operator can experiment with a promotion using a built in A-B testing capability referred to as “experiments,” as described herein. A promotion can be or include, for example, an incentive to upgrade to an annual plan for monthly plan members, a promotion code for users who have not yet made a purchase in an online service, and/or guidance shown to a subscribing user or member who clicks on a “cancel membership” button.

In various examples, the systems and methods described herein can implement a cross-device promotion in which (i) a uniqueness of the promotion can be tracked across all platforms (e.g., mobile, desktop, iOS, ANDROID, website, app, TV, and/or media streaming devices, etc.) that provide the promotion and (ii) any actions taken in response to the promotion can be applied across all platforms. For example, when a user is shown a promotion on a ROKU TV app, the promotion can be tracked across multiple platforms so that the user is not shown the same promotion on a desktop or iOS. Such tracking can be achieved by monitoring and storing user data for each individual user, to obtain a record of each user’s interactions with one or more online services.

To implement a cross-device promotion, an operator can use the admin console of the systems and methods described herein to designate a set of device platforms that can be used for a promotion. The devices can be or include, for example, desktop, iOS, mobile, tablet, ANDROID, TV, AMAZON FIRE TV, ROKU, and/or Apple TV. By way of user data import, a unique user identifier can be loaded into the systems and methods such that, when a user arrives on any platform, the user’s identifier can remain the same (e.g., a single user identifier used to track a user across all platforms). The user identifier can be used by the systems and methods described herein to determine whether or not to show the user a promotion on one platform that has already been presented to the user on a different platform.

In various examples, a “promotion interaction” can be or refer to interactions with past promotions that are tracked for use in user segments. Such interactions can include, for example, clicking on a button, accepting guidance, declining guidance, closing a promotion, or allowing the promotion to timeout. Promotions of any device platform can be used to build segments according to an operator’s specifications. User segments can be created, for example, based on specific promotions that have been presented to users, the specific devices on which the promotions were presented, and/or the user’s specific interactions with such promotions. In one example, if a user is shown a promotion on a ROKU TV app over a specific period of time, the user can be added to a segment for that promotion and device combination. Such information can be taken by an operator to determine whether to show a different or same promotion on the user’s desktop or iOS. Multiple profiles or segments can be created around the same user according to how the user interacted with previously seen promotions. For example, user segments can be created according to how users interact with content presented in the online services.

Paths

In various examples, a path (alternatively referred to as an “experience”) can be or include a guided experience to a targeted user that can be specified based on days, dates, visits, or time of day. Online service providers can have a desire to ensure that new users are progressively made aware of various benefits or features associated with the online services (e.g., for subscribers or members), to ensure that the users have an optimal experience with the service. For example, for a user in a 30-day trial, the online service provider may choose to (i) highlight unique content on the first day (e.g., with fewer ads), (ii) present a unique feature (e.g., content download capabilities) a week later, and (iii) reach out the user directly (e.g., by text, email, or phone) a week before the trial expires (e.g., if the user has not availed herself of all the benefits of the trial). Advantageously, the systems and methods described herein can make it possible for online service operators to programmatically configure and modify this type of experience on each of the respective online service platforms.

To implement a path, an operator can define a number of promotions or other content that will be included within the path. Each promotion can be initiated using a variety of triggers, including, for example: a time sequence in which promotions are presented to the user on a predefined timeline (e.g., with promotions 1, 2, and 3 presented on days 0, 7, and 30, respectively, measured from the user’s first visit), a usage-based sequence in which promotions are presented to the user when certain usage thresholds are met (e.g., promotion 1 shown on the user’s first visit to the service, promotion 2 shown on the user’s third visit, and promotion 3 shown upon the user’s first use of a service feature), and/or an interaction sequence in which promotions are presented based on interactions with one or more previous promotions (e.g., promotion 1 shown on the user’s first visit, promotion 2 shown on the user’s next visit if promotion 1 was accepted by the user, and promotion 3 shown on the user’s next visit if promotion 2 was declined). Any combination of time sequence triggers, usage-based triggers, and interaction sequence triggers can be used within a single path. In general, promotions are presented in the order defined within the path. A successive promotion may be shown, for example, only after the sequence trigger condition has been met and/or while the user remains active on the online service. In some examples, a path may be or include a type of promotion that has no visual or interactive elements.

In various examples, a path can be created by combining one or more actions with one or more segments, preferably for a specific duration and/or over a calendar schedule, and with a budgeted limit (e.g., Path = Segment(s) + Action(s) + Schedule(s) + Budget). Paths can include or be implemented to achieve, for example, VIP support with members only deals, extended trials, and/or limited discounts (e.g., VIP Path = High LTV Users + Members Only Deals + VIP Support + Peak Times + Limited to 10,000 Users). Paths can route segments of users to specific journeys through online services, websites, or applications. For example, all users may see a price increase during peak times (e.g., surge pricing), or a red carpet path may provide access to reserved items, premium support, and/or free shipping to VIP users. If a time schedule is specified for a path, then the path may be available only during those times and may expire at the end of the period. If a time schedule is not specified, the path may always be available.

In some examples, a budget for a path may include a user limit, which can be used by an operator to specify a maximum number of unique visitors that will be sent down a particular path. For example, a path called “Red Carpet” may give a special offer to high LTV users but be limited to the first 100 users who access the path.

In general, a user limit can be specified by number, by frequency, and/or by budget. A number limit can allow an operator to specific a maximum number of users who can take action on a promotion or path presented to them. For example, a number limit (e.g., 1000 users) can limit the total number of users (or total number of users in a segment) who receive the promotion. A frequency limit can allow an operator to restrict the total number of times a user can be shown a promotion with a specified period of time. For example, setting the frequency limit to two impressions over 30 days can result in a user seeing a promotion no more than twice over a 30-day period. The 30-day period can start when the first impression is shown. A budget limit can allow an operator to specify a consumable quantity that is decreased with each user action for a promotion or path. Budgets may be specified on a standalone basis or in concert with user limits. Budget limits may be useful when finances are an important consideration. For example, a budget set to $10,000 with a decrement of $10 can be equivalent to a $10,000 budget that is reduced $10 at a time.

In some implementations, a traffic split may be implemented that allows an operator to specify a portion (e.g., a percentage) of users who will be sent down a particular path. For example, a path called “VIP offer” may distribute a service feature to 10% of users in the VIP users segment. This approach can be used to determine how well received an offer is before doing a full rollout.

Schedule

In some examples, a schedule can allow an operator to restrict content, such as a promotion or path, to a specific time schedule. For example, the operator can restrict a promotion or path to be active between Oct. 1, 2020 to Dec. 31, 2020, and further limited to Thursdays, Fridays, and Saturdays between 6 pm and 11 pm. A time schedule can include a date window, a day part, and a time part. The date window can specify, for example, a start date and an end date within which the promotion or path can be active and outside of which the promotion or path can be inactive. The day part can be used to specify days of the week when the promotion or path is active. The time part can be used to specify times of the day when the promotion or path is active. In some examples, an operator can specify whether a schedule is based on a time zone associated with user’s client device or client application or on a time zone associated with the operator.

In some implementations, an omni-channel frequency cap can be implemented that limits how many times a user can see one or more promotions. The promotion frequency cap can enable an operator of an online service to specify a maximum number of times a user can see a single promotion. Likewise, a global frequency cap can enable the operator to specify a maximum number of times a user can see all eligible promotions. For example, for a segment of users, an online service provider may choose to show one promotion once a day and a second promotion twice within a seven day period, with a maximum of 30 promotions in 30 days. Such frequency caps can reduce the number of times a user sees the same promotion, which can help ensure a better user experience with less promotion fatigue. The frequency criteria can be applied automatically to cover over all device platforms used by the online service.

To implement a frequency cap, an operator of an online service provider can use the systems and methods described herein to define a frequency cap on specific promotions (e.g., limit iOS promotion 1 to once per 7 days, limit ROKU promotion 2 to twice per 7 days, etc.). The operator can also define a global frequency cap, if desired (e.g., limit all promotions to 3 times per 7 days). In this example, a user’s interaction sequence with content can be as follows: the user visits iOS on Day 1 and receives promotion 1; the user visits ROKU on Day 2 and receives promotion 2; the user visits iOS on Day 3 and receives promotion 1; and the user visits ROKU on day 4 but does not receive further promotions until the global frequency cap resets on day 8.

Experiments

In certain examples, the systems and methods described herein can be used to perform “experiments” that allow online service operators to test variations of a promotion or experience to see which variation results in the greatest positive impact. Operators can specify any number of variations within an experiment but, in general, only one experiment may run on a given promotion or path at a given instant in time. This allows operators to run multiple experiments or trials over time on a single promotion or path. In various implementations, an experiment may be designed to modify one or more of the following parameters associated with a promotion or other item of content, such as, for example: title text, message body text, offer value, confirm button text, and/or cancel button text. An experiment may modify any actions specified within a path. Experiments can allocate users equally to each variation.

Retention Cohorts

In certain implementations, the systems and methods described herein can include a user interface that allows online service operators to compare the activities or status of (i) cohorts of users who have interacted with specific promotions (or guidance or other items of content) with (ii) cohorts of users who have not interacted with the specific promotions and/or have interacted with one or more different promotions. Such comparisons can allow an operator to evaluate the impact such interactions can have on user engagement with and retention in the online service. For example, such comparisons can allow the service operator to determine whether users who interact with promotion A stay with the online service longer that those who interact with promotion B.

FIG. 5 includes a screenshot 500 of an example graphical user interface that allows an operator to visualize the influence that two different promotions, paths, actions, or other items of content have on user retention in an online service over time. Each row in the user interface represents a date, and columns represent days since an initial date. Each cell or row-column combination includes a box 502 formed by two triangles. Numbers in each triangle can provide an indication of user engagement with the online service. One of the triangles 504 (e.g., a top triangle) can represent engagement following one promotion, and the other triangle 506 (e.g., a bottom triangle) can represent engagement following a different promotion. In the depicted example, one of the promotions was more effective at improving user engagement with the online service. Such information can be used by the operator to adjust future content presentations, in an effort to improve user retention.

Pricing

In some instances, the systems and methods described herein can utilize or include a pricing module for connecting to a payment system and/or a CRM system. Such connections can allow user properties such as user LTV and days to next renewal to be retrieved, but the connections may also provide granular control over pricing, coupons, and subscriptions. Pricing traits can include, for example, user LTV, user since (e.g., length of user relationship with service), is delinquent (e.g., true if the last billing attempt was unsuccessful), days to next renewal, and/or trial about to end (e.g., true if trial ends within three days). Pricing actions can include, for example, show/hide coupons (e.g., show or hide the coupon field), show/hide subscription plans (e.g., show only specific subscription plans or hide irrelevant subscription plans), ask payer for credit card postal code (e.g., show a postal code field on a payment form) ask payer for billing address (e.g., show all address fields, such as street address, city, state, postal code), ask payer for phone number (e.g., show a phone number field on the payment form), ask payer for email address (e.g., show an email field on the payment form), and/or increment usage quantity (e.g., add metered billing usage or allow user to view a video 10 times and then charge $100 dollars before allowing for more consumption).

Technology

In certain examples, the systems and methods can provide machine intelligence that enables online service providers to be richer, self-sufficient, more private, and/or less dependent on congestion prone single points of processing. The systems and methods can be an ideal fit for gaming, e-commerce, communications, financial services, entertainment (e.g., movies and music), education, online storage, billing, customer relationship management (CRM), and/or marketplace online services. The systems and methods can be incorporated into or accessed by (i) a website using a tag manager or (ii) an app using a software development kit (SDK). Alternatively or additionally, the systems and methods or portions thereof can be embedded directly on a web server (e.g., as a plugin).

In certain examples, the software and database components of the system 100 can utilize or include client libraries, an admin console, a real-time stream data processor, integrations with various third party business systems (e.g., for billing, support, or CRM services), and/or a webserver plugin. The client libraries can act as data collectors and/or render end user facing content and experiences. A JAVASCRIPT client, for example, can be added using a GOOGLE tag manager or a TEALIUM tag manager, or can be directly integrated into an online service provider’s website. The admin console can be or provide a management console and/or nerve center of an online service platform. The admin console can allow operators of online services to configure apps and set up traits, segments, promotions, experiences, and integrations. The admin console can allow operators to view dashboards and run experiments. The real-time stream data processor can dynamically segment incoming traffic into manageable user segments based on rules or machine learning. In some examples, the real-time stream data processor can be incorporated into the tracking module 120 and/or the segment module 122. Additionally or alternatively, integrations with various third party business systems can be achieved that relate to, for example, billing (e.g., STRIPE, APPLE, or ANDROID), support (e.g., ZENDESK, FRESHDESK, or SALESFORCE.COM), and/or marketing (e.g., SENDGRID, CAMPAIGN MONITOR, or MAILCHIMP). These integrations can be used or accessed through third party connectors, as described herein. A webserver plugin can be used to extract crucial information from business systems or databases and securely add such information to incoming HTTP requests (e.g., for handling an online service provider’s application server logic).

FIG. 6 is a flowchart of an example method 600 of monitoring and managing an online experience. User data is obtained (step 602) that describes interactions between a plurality of users and plurality of online services. A rate of change in usage of the online services is determined (step 604) based on the user data for each user in the plurality of users. A portion of the users is assigned (step 606) to a user segment based on the user data and the determined rate of change in usage. Based on the assignment, each user in the user segment is provided (step 608) with a customized experience in one or more of the online services.

Computer-Based Implementations

In some examples, some or all of the processing described above can be carried out on a personal computing device, on one or more centralized computing devices, or via cloud-based processing by one or more servers. Some types of processing can occur on one device and other types of processing can occur on another device. Some or all of the data described above can be stored on a personal computing device, in data storage hosted on one or more centralized computing devices, and/or via cloud-based storage. Some data can be stored in one location and other data can be stored in another location. In some examples, quantum computing can be used and/or functional programming languages can be used. Electrical memory, such as flash-based memory, can be used.

FIG. 7 is a block diagram of an example computer system 700 that may be used in implementing the technology described herein. General-purpose computers, network appliances, mobile devices, or other electronic systems may also include at least portions of the system 700. The system 700 includes a processor 710, a memory 720, a storage device 730, and an input/output device 740. Each of the components 710, 720, 730, and 740 may be interconnected, for example, using a system bus 750. The processor 710 is capable of processing instructions for execution within the system 700. In some implementations, the processor 710 is a single-threaded processor. In some implementations, the processor 710 is a multi-threaded processor. The processor 710 is capable of processing instructions stored in the memory 720 or on the storage device 730.

The memory 720 stores information within the system 700. In some implementations, the memory 720 is a non-transitory computer-readable medium. In some implementations, the memory 720 is a volatile memory unit. In some implementations, the memory 720 is a non-volatile memory unit.

The storage device 730 is capable of providing mass storage for the system 700. In some implementations, the storage device 730 is a non-transitory computer-readable medium. In various different implementations, the storage device 730 may include, for example, a hard disk device, an optical disk device, a solid-state drive, a flash drive, or some other large capacity storage device. For example, the storage device may store long-term data (e.g., database data, file system data, etc.). The input/output device 740 provides input/output operations for the system 700. In some implementations, the input/output device 740 may include one or more network interface devices, e.g., an Ethernet card, a serial communication device, e.g., an RS-232 port, and/or a wireless interface device, e.g., an 802.11 card, a 3G wireless modem, or a 4G wireless modem. In some implementations, the input/output device may include driver devices configured to receive input data and send output data to other input/output devices, e.g., keyboard, printer and display devices 760. In some examples, mobile computing devices, mobile communication devices, and other devices may be used.

In some implementations, at least a portion of the approaches described above may be realized by instructions that upon execution cause one or more processing devices to carry out the processes and functions described above. Such instructions may include, for example, interpreted instructions such as script instructions, or executable code, or other instructions stored in a non-transitory computer readable medium. The storage device 730 may be implemented in a distributed way over a network, such as a server farm or a set of widely distributed servers, or may be implemented in a single computing device.

Although an example processing system has been described in FIG. 7 , embodiments of the subject matter, functional operations and processes described in this specification can be implemented in other types of digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible nonvolatile program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

The term “system” may encompass all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A processing system may include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). A processing system may include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

A computer program (which may also be referred to or described as a program, software, a software application, a module, a software module, a script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

Computers suitable for the execution of a computer program can include, by way of example, general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. A computer generally includes a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few.

Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user’s user device in response to requests received from the web browser.

Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous. Other steps or stages may be provided, or steps or stages may be eliminated, from the described processes. Accordingly, other implementations are within the scope of the following claims.

Terminology

The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

The term “approximately”, the phrase “approximately equal to”, and other similar phrases, as used in the specification and the claims (e.g., “X has a value of approximately Y” or “X is approximately equal to Y”), should be understood to mean that one value (X) is within a predetermined range of another value (Y). The predetermined range may be plus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unless otherwise indicated.

The indefinite articles “a” and “an,” as used in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and/or,” as used in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof, is meant to encompass the items listed thereafter and additional items.

Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Ordinal terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term), to distinguish the claim elements.

Each numerical value presented herein, for example, in a table, a chart, or a graph, is contemplated to represent a minimum value or a maximum value in a range for a corresponding parameter. Accordingly, when added to the claims, the numerical value provides express support for claiming the range, which may lie above or below the numerical value, in accordance with the teachings herein. Absent inclusion in the claims, each numerical value presented herein is not to be considered limiting in any regard.

The terms and expressions employed herein are used as terms and expressions of description and not of limitation, and there is no intention, in the use of such terms and expressions, of excluding any equivalents of the features shown and described or portions thereof. In addition, having described certain embodiments of the invention, it will be apparent to those of ordinary skill in the art that other embodiments incorporating the concepts disclosed herein may be used without departing from the spirit and scope of the invention. The features and functions of the various embodiments may be arranged in various combinations and permutations, and all are considered to be within the scope of the disclosed invention. Accordingly, the described embodiments are to be considered in all respects as only illustrative and not restrictive. Furthermore, the configurations, materials, and dimensions described herein are intended as illustrative and in no way limiting. Similarly, although physical explanations have been provided for explanatory purposes, there is no intent to be bound by any particular theory or mechanism, or to limit the claims in accordance therewith. 

What is claimed is:
 1. A computer-implemented method of managing user experiences for a plurality of online services, the method comprising: providing a platform to a plurality of operators for a plurality of online services, each operator corresponding to a respective online service from the plurality of online services, the online services comprising at least one of a website, a mobile application, or a streaming service; providing, using the platform, a first graphical user interface configured to display user traits for users of the online services; providing, using the platform, a second graphical user interface configured to display actions to be performed by the online services; enabling, through use of the platform, one of the operators from one of the online services to select (i) a combination of user traits using the first graphical user interface and (ii) at least one action using the second graphical user interface, wherein, following the selection, the at least one action is configured to be performed by the one of the online services for a group of users corresponding to the combination of user traits.
 2. The method of claim 1, wherein the platform comprises a cloud infrastructure platform that enables real-time targeting of users for monitoring and managing an online user experience.
 3. The method of claim 1, wherein a trait in the combination of user traits comprises at least one user attribute or user behavior.
 4. The method of claim 3, wherein the trait comprises at least one of a device type, a lifetime value, a fraud score, a chum score, a net promoter score, or a customer satisfaction score.
 5. The method of claim 1, wherein the at least one action comprises one or more steps that are configured to be performed by the one of the online services.
 6. The method of claim 1, wherein the at least one action is configured to be performed in response to user-interaction with content in the one of the online services.
 7. The method of claim 1, wherein the at least one action comprises a hidden action or a cascading action.
 8. The method of claim 1, wherein the at least one action comprises an incentive to increase engagement with the one of the online services.
 9. The method of claim 1, wherein the group of users comprises a user segment sharing the combination of user traits.
 10. A system comprising: one or more computer systems programmed to perform operations comprising: providing a platform to a plurality of operators for a plurality of online services, each operator corresponding to a respective online service from the plurality of online services, the online services comprising at least one of a website, a mobile application, or a streaming service; providing, using the platform, a first graphical user interface configured to display user traits for users of the online services; providing, using the platform, a second graphical user interface configured to display actions to be performed by the online services; enabling, through use of the platform, one of the operators from one of the online services to select (i) a combination of user traits using the first graphical user interface and (ii) at least one action using the second graphical user interface, wherein, following the selection, the at least one action is configured to be performed by the one of the online services for a group of users corresponding to the combination of user traits.
 11. The system of claim 10, wherein the platform comprises a cloud infrastructure platform that enables real-time targeting of users for monitoring and managing an online user experience.
 12. The system of claim 10, wherein a trait in the combination of user traits comprises at least one user attribute or user behavior.
 13. The system of claim 12, wherein the trait comprises at least one of a device type, a lifetime value, a fraud score, a chum score, a net promoter score, or a customer satisfaction score.
 14. The system of claim 10, wherein the at least one action comprises one or more steps that are configured to be performed by the one of the online services.
 15. The system of claim 10, wherein the at least one action is configured to be performed in response to user-interaction with content in the one of the online services.
 16. The system of claim 10, wherein the at least one action comprises a hidden action or a cascading action.
 17. The system of claim 10, wherein the at least one action comprises an incentive to increase engagement with the one of the online services.
 18. The system of claim 10, wherein the group of users comprises a user segment sharing the combination of user traits.
 19. A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the one or more computer processors to perform operations comprising: providing a platform to a plurality of operators for a plurality of online services, each operator corresponding to a respective online service from the plurality of online services, the online services comprising at least one of a website, a mobile application, or a streaming service; providing, using the platform, a first graphical user interface configured to display user traits for users of the online services; providing, using the platform, a second graphical user interface configured to display actions to be performed by the online services; enabling, through use of the platform, one of the operators from one of the online services to select (i) a combination of user traits using the first graphical user interface and (ii) at least one action using the second graphical user interface, wherein, following the selection, the at least one action is configured to be performed by the one of the online services for a group of users corresponding to the combination of user traits. 